Integrating Metabolic Modeling and Targeted Supplementation for the Rapid Detection of Clostridium tyrobutyricum in Dairy Products

Clostridium tyrobutyricum is a major cause of late blowing defects (LBDs) in cheese, resulting in substantial economic losses. Early detection is critical for maintaining product quality. In this study, we developed a rapid detection approach integrating genome-scale metabolic modeling (GEM) with systematic culture optimization. Three media were evaluated, identifying RCM at 38.5 °C as the optimal condition for reducing the lag phase. Flux Balance Analysis (FBA) revealed that targeted supplementation with magnesium, zinc, Vitamin B6, and L-tryptophan significantly enhanced metabolic flux through nucleotide biosynthesis and energy transfer pathways, particularly reaction rxn01219_c0. Validation using artificially contaminated milk confirmed that the optimized 0.5× supplementation mixture synergistically reduced detection time by approximately 35 h compared to conventional MPN methods. This study demonstrates that bridging systems biology with traditional microbiology provides a cost-effective and mechanistic framework for rapid pathogen detection in the dairy industry.